Gemini CLI Memory Explained: Fix MCP Leaks, Master Persistent Context & Boost AI Development

Introduction: Revolutionizing Terminal-Based AI with Advanced Memory Systems

Google's Gemini CLI has emerged as a game-changing tool for developers, bringing the power of Gemini AI directly into the terminal environment. One of its most significant features is its sophisticated memory management system, which has received substantial updates in recent releases. This comprehensive guide explores everything you need to know about Gemini CLI's memory capabilities, including the latest updates released by Google in 2025-2026.

What is Gemini CLI?

Gemini CLI is an open-source AI agent that integrates Google's powerful Gemini models directly into your command-line interface. Designed for developers who live in the terminal, it offers:

Access to Gemini 3 models with improved reasoning capabilities
1 million token context window for processing entire codebases
Built-in tools for file operations, shell commands, and web fetching
MCP (Model Context Protocol) support for custom integrations
Extensible architecture for adding custom functionality

Understanding Memory in Gemini CLI: Two Distinct Concepts

One of the most important aspects to understand about Gemini CLI is the distinction between "context" and "memory" – terms often used interchangeably but representing different mechanisms within the tool.

Short-Term Memory: Context

Context represents ephemeral, short-term information that the model uses during a single continuous interaction. Think of it as the model's "working memory" – everything it can "see" at any given moment.

Key characteristics:

Volatile: Lost when you restart Gemini CLI or clear context with /clear
Massive capacity: Gemini 2.5 Pro features a 1-million-token context window
Dynamic: Assembled from multiple sources during conversation

Long-Term Memory: Persistent Storage

Memory in Gemini CLI refers to persistent information stored across sessions through GEMINI.md files and the memory system. This is the tool's "long-term memory."

The GEMINI.md File System: Core of Persistent Memory

What is GEMINI.md?

GEMINI.md files are Markdown documents that provide persistent instructional context to the Gemini model. They allow you to define:

Project-specific coding standards
Preferred frameworks and libraries
API conventions and patterns
Persona or behavior guidelines
Team coding style guides

Hierarchical Memory Loading

Gemini CLI uses a sophisticated hierarchical system to load context files:

1. Global Context

Location: ~/.gemini/GEMINI.md Scope: User-wide rules applying to all projects Example use: "Always use TypeScript and Jest for testing"

2. Project/Ancestor Context

Location: Current working directory and parent directories Scope: Project-specific instructions Search pattern: Searches upward until reaching project root (identified by .git folder)

3. Sub-directory Context

Location: Subdirectories below current working directory Scope: Component or module-specific instructions Purpose: Highly specific instructions for individual parts of codebase

Creating Effective GEMINI.md Files

Here's an example of a well-structured GEMINI.md file:

# Project: My TypeScript Library

## General Instructions
- When you generate new TypeScript code, follow the existing coding style
- Ensure all new functions and classes have JSDoc comments
- Prefer functional programming paradigms where appropriate

## Coding Style
- Use 2 spaces for indentation
- Prefix interface names with `I` (for example, `IUserService`)
- Always use strict equality (`===` and `!==`)

## Database Configuration
- Database: PostgreSQL on port 5432
- Always use parameterized queries for security

## Testing Standards
- Write unit tests for all new functions
- Use Jest as the testing framework
- Aim for 80% code coverage

Memory Import Processor: Modularizing Context

For large projects, you can break down GEMINI.md files into smaller components using the @ import syntax:

# Main GEMINI.md file

This is the main content.

@./components/instructions.md

More content here.

@../shared/style-guide.md

This feature supports both relative and absolute paths, enabling better organization of context files.

Latest Memory Commands and Features

The /memory Command Suite

Google has enhanced the memory management system with several powerful commands:

/memory add

Purpose: Add facts or notes to persistent memory Usage: /memory add <text> Example: /memory add "My preferred CSS framework is Tailwind CSS"

This command appends text to your global ~/.gemini/GEMINI.md file under a ## Gemini Added Memories section. Once added, the AI sees this information in all future sessions.

/memory show

Purpose: Display full concatenated memory content Usage: /memory show Function: Shows the exact instructional context being provided to the model, including all loaded GEMINI.md files from the hierarchy

/memory refresh

Purpose: Force re-scan and reload of all GEMINI.md files Usage: /memory refresh Function: Updates the AI's instructional context by reloading all configured locations

/memory list

Purpose: Find location of long-term memory files Usage: /memory list Function: Quickly displays where your memory files are stored

Recent Memory-Related Updates (2025-2026)

Critical MCP Transport Memory Leak Fix

One of the most significant updates in early 2026 was the fix for MCP transport memory leaks. This update addressed a critical issue where:

MCP (Model Context Protocol) connections weren't being properly closed
Long-running sessions experienced memory accumulation
System resources were gradually depleted

Technical Details:

Pull Request: #18054
Impact: Ensures MCP transport is properly closed to prevent memory leaks
Benefit: Improved stability for long-running sessions with MCP servers

This fix is particularly important for developers using multiple MCP servers or running extended development sessions.

Enhanced Memory List Command

The addition of the /memory list command provides quick visibility into your memory file locations, making it easier to:

Locate global memory files
Find project-specific context files
Debug memory loading issues
Verify which context files are active

Improved Memory Refresh Functionality

Updates to the /memory refresh command have improved how context files are reloaded:

More reliable detection of file changes
Better error handling for malformed GEMINI.md files
Faster refresh operations
Clearer feedback when context is updated

Advanced Memory Management: Conversation Branching

Chat Session Management

Gemini CLI allows sophisticated conversation management through /chat commands:

/chat save

Purpose: Save current conversation state Usage: /chat save <tag> Example: /chat save memory-leak-bug

/chat resume

Purpose: Resume a saved conversation Usage: /chat resume <tag> Function: Restores conversation exactly where you left off

/chat list

Purpose: View all saved conversation tags Usage: /chat list Benefit: Organize multiple parallel development contexts

Practical Workflow Example

# Working on feature A
gemini
> implementing user authentication...
> /chat save auth-feature

# Switch to urgent bug fix
> /clear
> fixing memory leak in payment service...
> /chat save payment-bug

# Return to feature A later
> /chat resume auth-feature
# Conversation continues exactly where you left off

Gemini Code Assist: Persistent Memory for Code Reviews

Memory-Enabled AI Code Reviews

In November 2025, Google released a groundbreaking persistent memory feature for Gemini Code Assist on GitHub. This transforms code review agents from stateless tools into long-term project contributors.

How Persistent Memory Works

1. Automatic Learning from Pull Requests

When a PR is merged, Gemini analyzes comment threads
Identifies feedback patterns and team preferences
Stores learned rules in secure, project-specific storage

2. Rule Inference

Example interaction:

Gemini suggests: "Do not line-wrap import statements"
Developer responds: "We actually prefer line-wrapped imports in Java"
Gemini infers rule: "In Java, import statements could be line-wrapped"

3. Rule Application

Initial review guidance: Rules shape analysis before it begins
Suggestion filtering: Rules filter out suggestions that violate learned best practices

Static vs. Dynamic Memory

styleguide.md (Static):

Always added to agent's prompt
Suitable for universal, unchanging guidelines
Requires manual maintenance

Persistent Memory (Dynamic):

Automatically extracted from interactions
Retrieved only when relevant
Scales efficiently with team size
No manual maintenance required

Memory Optimization Best Practices

1. Organize Memory Hierarchically

~/.gemini/GEMINI.md          # Global preferences
~/projects/
  my-app/
    GEMINI.md                # Project standards
    frontend/
      GEMINI.md              # Frontend-specific rules
    backend/
      GEMINI.md              # Backend-specific rules

2. Use Concise, Actionable Facts

Good: /memory add "Use Prisma ORM for database operations" Poor: /memory add "I was thinking that maybe we should probably use Prisma for database stuff"

3. Version Control Your Context Files

Add GEMINI.md files to your Git repository:

Share context across team members
Track evolution of coding standards
Enable consistent AI behavior across team

4. Regular Memory Audits

Periodically review your memory files:

# Check what's in memory
/memory show

# Edit if needed
vim ~/.gemini/GEMINI.md

# Reload changes
/memory refresh

Checkpointing: Time-Travel for Your Workspace

The /checkpoint Feature

Gemini CLI includes a powerful checkpointing system for managing workspace state:

/checkpoint create

Purpose: Capture current state before risky operations What's saved:

File system state
Conversation context
Git snapshot

/checkpoint list

Purpose: View all saved checkpoints Display: Timestamps and descriptions

/checkpoint restore

Purpose: Rollback to previous state Usage: /restore 0 (where 0 is checkpoint index)

Practical Use Case

# Before major refactoring
> /checkpoint create
Checkpoint saved: [2026-02-11 10:30:15] Before refactoring auth module

# Attempt risky changes
> refactor the authentication module to use OAuth2

# If something goes wrong
> /restore 0
# Everything rolls back: files AND conversation state

Context Compression: Managing Token Limits

Automatic Context Management

When context exceeds 70% of the model's limit:

Auto-trigger: Compression activates automatically
Method: AI summarizes conversation into structured XML snapshot
Preserves: Goal, key knowledge, file state, current plan

Manual Compression

/compress

This command forces compression even below the threshold, useful for:

Long-running conversations
Before major context switches
Cleaning up verbose chat history

MCP Integration and Memory

Model Context Protocol (MCP)

MCP servers extend Gemini CLI's capabilities by connecting to external services. Recent updates improve memory management in MCP interactions:

Memory-Safe MCP Operations

Key improvements:

Bounded caches with automatic cleanup
O(1) operations for rate limiting
Prevention of memory accumulation
Graceful degradation under memory pressure

Configuring MCP Servers

Example settings.json configuration:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}"
      }
    },
    "database": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": {
        "DATABASE_URL": "${DB_CONNECTION_STRING}"
      }
    }
  }
}

Troubleshooting Memory Issues

Common Issues and Solutions

Issue 1: Memory Not Being Recalled

Symptoms: Facts added via /memory add aren't used by model

Solutions:

Verify file location: cat ~/.gemini/GEMINI.md
Force refresh: /memory refresh
Use specific prompting: "Use my preferred CSS framework"
Check for typos in GEMINI.md

Issue 2: Context Window Overflow

Symptoms: Error messages about token limits

Solutions:

Use /compress to condense conversation
Clear unnecessary context: /clear
Remove verbose GEMINI.md content
Split large files using imports

Issue 3: Slow Performance

Symptoms: Laggy responses, high memory usage

Solutions:

Check for MCP memory leaks (ensure latest version)
Reduce context file size
Close unused MCP connections
Use checkpoints to reset state

Security Considerations

Memory and Sensitive Data

Best practices:

Never store API keys in GEMINI.md files
Use environment variables for credentials
Be cautious with shared memory in team settings
Regular audits of stored memory content

Environment Variable Redaction

Gemini CLI automatically redacts sensitive environment variables:

GEMINI_API_KEY
GOOGLE_API_KEY
Patterns matching *TOKEN*, *SECRET*, *PASSWORD*

This prevents unintended credential exposure to third-party MCP servers.

Memory in Enterprise Settings

Team Collaboration

Shared context files:

Commit GEMINI.md to version control
Standardize team coding practices
Enable consistent AI behavior across team
Document project conventions

Private memory:

Keep personal preferences in ~/.gemini/GEMINI.md
Project-specific rules in project GEMINI.md
Balance between team standards and individual preferences

Future Developments

Roadmap Indicators

Based on recent updates and community feedback, future memory enhancements may include:

Enhanced memory search: Better retrieval of relevant context
Cross-session learning: Improved ability to learn from past interactions
Memory analytics: Insights into what context is most useful
Collaborative memory: Team-wide shared memory with access controls

Performance Optimization

Memory Loading Optimization

Configure memory loading behavior in settings.json:

{
  "context": {
    "hierarchical": true,
    "respectGitignore": true,
    "respectGeminiIgnore": true,
    "recursiveFileSearch": true
  }
}

Options explained:

hierarchical: Enable directory scanning for GEMINI.md
respectGitignore: Skip files/folders in .gitignore
respectGeminiIgnore: Honor .geminiignore files
recursiveFileSearch: Enable recursive file discovery

Real-World Use Cases

Use Case 1: Multi-Project Developer

Scenario: Developer working on multiple projects with different standards

Solution:

# Global preferences
~/.gemini/GEMINI.md: "I prefer functional programming"

# Project A (React)
~/projects/react-app/GEMINI.md: "Use React hooks, not class components"

# Project B (Vue)
~/projects/vue-app/GEMINI.md: "Use Vue 3 Composition API"

Use Case 2: Team Onboarding

Scenario: New team member needs to learn project conventions

Solution:

Clone repository (includes GEMINI.md)
Install Gemini CLI
Run gemini in project directory
AI automatically knows team standards
Consistent code suggestions from day one

Use Case 3: Code Review Consistency

Scenario: Want AI code reviews to remember team preferences

Solution:

Enable Gemini Code Assist on GitHub
Activate persistent memory
AI learns from merged PR feedback
Future reviews align with team standards

Monitoring and Observability

Memory Usage Monitoring

Check memory status in CLI footer:

Shows count of loaded context files
Visual indicator of active context
Quick verification of memory configuration

Telemetry Insights

For enterprise deployments, leverage telemetry:

Track memory file usage patterns
Identify most valuable context
Optimize memory configuration
Monitor performance impact

Conclusion

Gemini CLI's memory system represents a significant advancement in AI-assisted development. The recent updates, particularly the MCP transport memory leak fixes and enhanced memory commands, make it more reliable and powerful than ever.

Key takeaways:

GEMINI.md files provide persistent, hierarchical context
Memory commands offer granular control over what AI remembers
Recent fixes address critical stability issues
Persistent memory in Code Assist enables learning from team interactions
Proper memory management is essential for optimal performance

By mastering these memory features, developers can create a truly personalized AI assistant that understands their projects, follows their conventions, and continuously improves through feedback.

Getting Started with Memory Management

Ready to leverage these powerful memory features? Here's your quick-start checklist:

Install/Update Gemini CLI:

npm install -g @google/gemini-cli
gemini -v  # Verify version 0.23.0 or later

Create Your Global Memory:

echo "# My Global Preferences" > ~/.gemini/GEMINI.md
echo "- I prefer TypeScript over JavaScript" >> ~/.gemini/GEMINI.md

Add Project Context:

cd your-project
echo "# Project Standards" > GEMINI.md
echo "- Use ESLint with Airbnb config" >> GEMINI.md

Verify Memory Loading:
```
gemini
> /memory show
```
Start Building: Let Gemini CLI remember your preferences and work the way you do!

Google Gemini

Related Topics

Gemini CLI Memory Explained: Fix MCP Leaks, Master Persistent Context & Boost AI Development